Assessing the Usefulness of Digital Contact Tracing Using Real-World Contact Data

The significant impact of the global health crisis caused by the novel coronavirus (SARS-CoV-2) on health systems and social behaviors has prompted many countries to adopt digital contact tracing (DCT) technologies as a complement to traditional epidemiological methods. This study evaluates the effectiveness and limitations of Peru’s DCT approach using real-world data from 1,660,000 users, including 80,068 confirmed cases of Covid-19. Our study provides original insights into the complex relationship between contact patterns and contagion dynamics based on data collected from Peru's DCT application in 2020. Using temporal network reconstruction via the Link Stream formalism, we thoroughly investigated the temporal interaction networks of DCT users and found that, on average, individuals who contracted the virus had more encounters than those who did not. Our research aims to understand how mobility among DCT users evolved throughout the pandemic and how contact patterns emerged within the collected data. Although limited user engagement hindered precise, individual-level tracing assessments, our findings suggest that the DCT tool has considerable potential to inform public policy on a broader scale. Additionally, the study explores the complex interplay between mobility and socioeconomic indicators, revealing significant disparities in movement patterns among different socioeconomic groups. Finally, we provide the community with a dataset containing our reconstructed large contact networks alongside the given infection status to foster future research on the development of enhanced DCT tools.